Due to sensitivity in occlusion, the difference in lighting condition, deformation in object pose, features from tracklets may have much difference even though they are from the same object. To address such issues, self-attention is an appropriate strategy for tracklet embedding. Inspired from the transformer [1]}, a multi-head self-attention mechanism is employed for tracklet embedding in the proposed Connector model.
The embedding framework is shown in Figure REF .
